Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations178
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.6 KiB
Average record size in memory112.7 B

Variable types

Numeric13
Categorical1

Alerts

Alcohol is highly overall correlated with Color_intensity and 2 other fieldsHigh correlation
Malicacid is highly overall correlated with HueHigh correlation
Alcalinity_of_ash is highly overall correlated with Ash and 3 other fieldsHigh correlation
Total_phenols is highly overall correlated with 0D280_0D315_of_diluted_wines and 3 other fieldsHigh correlation
Flavanoids is highly overall correlated with 0D280_0D315_of_diluted_wines and 5 other fieldsHigh correlation
Nonflavanoid_phenols is highly overall correlated with FlavanoidsHigh correlation
Proanthocyanins is highly overall correlated with 0D280_0D315_of_diluted_wines and 2 other fieldsHigh correlation
Color_intensity is highly overall correlated with Alcohol and 1 other fieldsHigh correlation
Hue is highly overall correlated with Flavanoids and 2 other fieldsHigh correlation
0D280_0D315_of_diluted_wines is highly overall correlated with Flavanoids and 3 other fieldsHigh correlation
Proline is highly overall correlated with Alcohol and 2 other fieldsHigh correlation
class is highly overall correlated with 0D280_0D315_of_diluted_wines and 6 other fieldsHigh correlation
Magnesium is highly overall correlated with ProlineHigh correlation
Ash is highly overall correlated with Alcalinity_of_ashHigh correlation

Reproduction

Analysis started2025-05-07 16:14:26.560410
Analysis finished2025-05-07 16:14:34.811040
Duration8.25 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Alcohol
Real number (ℝ)

High correlation 

Distinct126
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.000618
Minimum11.03
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:34.867577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.6585
Q112.3625
median13.05
Q313.6775
95-th percentile14.2215
Maximum14.83
Range3.8
Interquartile range (IQR)1.315

Descriptive statistics

Standard deviation0.81182654
Coefficient of variation (CV)0.062445227
Kurtosis-0.85249957
Mean13.000618
Median Absolute Deviation (MAD)0.68
Skewness-0.051482331
Sum2314.11
Variance0.65906233
MonotonicityNot monotonic
2025-05-07T12:14:34.943328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.05 6
 
3.4%
12.37 6
 
3.4%
12.08 5
 
2.8%
12.29 4
 
2.2%
12.42 3
 
1.7%
12.25 3
 
1.7%
12 3
 
1.7%
12.33 2
 
1.1%
13.17 2
 
1.1%
13.73 2
 
1.1%
Other values (116) 142
79.8%
ValueCountFrequency (%)
11.03 1
0.6%
11.41 1
0.6%
11.45 1
0.6%
11.46 1
0.6%
11.56 1
0.6%
11.61 1
0.6%
11.62 1
0.6%
11.64 1
0.6%
11.65 1
0.6%
11.66 1
0.6%
ValueCountFrequency (%)
14.83 1
0.6%
14.75 1
0.6%
14.39 1
0.6%
14.38 2
1.1%
14.37 1
0.6%
14.34 1
0.6%
14.3 1
0.6%
14.23 1
0.6%
14.22 2
1.1%
14.21 1
0.6%

Malicacid
Real number (ℝ)

High correlation 

Distinct133
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3363483
Minimum0.74
Maximum5.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:35.015212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile1.061
Q11.6025
median1.865
Q33.0825
95-th percentile4.4555
Maximum5.8
Range5.06
Interquartile range (IQR)1.48

Descriptive statistics

Standard deviation1.1171461
Coefficient of variation (CV)0.47815905
Kurtosis0.29920668
Mean2.3363483
Median Absolute Deviation (MAD)0.52
Skewness1.0396512
Sum415.87
Variance1.2480154
MonotonicityNot monotonic
2025-05-07T12:14:35.084629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.73 7
 
3.9%
1.67 4
 
2.2%
1.81 4
 
2.2%
1.68 3
 
1.7%
1.61 3
 
1.7%
1.51 3
 
1.7%
1.9 3
 
1.7%
1.35 3
 
1.7%
1.53 3
 
1.7%
1.65 2
 
1.1%
Other values (123) 143
80.3%
ValueCountFrequency (%)
0.74 1
0.6%
0.89 1
0.6%
0.9 1
0.6%
0.92 1
0.6%
0.94 2
1.1%
0.98 1
0.6%
0.99 1
0.6%
1.01 1
0.6%
1.07 1
0.6%
1.09 1
0.6%
ValueCountFrequency (%)
5.8 1
0.6%
5.65 1
0.6%
5.51 1
0.6%
5.19 1
0.6%
5.04 1
0.6%
4.95 1
0.6%
4.72 1
0.6%
4.61 1
0.6%
4.6 1
0.6%
4.43 1
0.6%

Ash
Real number (ℝ)

High correlation 

Distinct79
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3665169
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:35.150171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.92
Q12.21
median2.36
Q32.5575
95-th percentile2.7415
Maximum3.23
Range1.87
Interquartile range (IQR)0.3475

Descriptive statistics

Standard deviation0.27434401
Coefficient of variation (CV)0.11592734
Kurtosis1.1439782
Mean2.3665169
Median Absolute Deviation (MAD)0.16
Skewness-0.17669932
Sum421.24
Variance0.075264635
MonotonicityNot monotonic
2025-05-07T12:14:35.222356image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3 7
 
3.9%
2.28 7
 
3.9%
2.7 6
 
3.4%
2.32 6
 
3.4%
2.36 6
 
3.4%
2.2 5
 
2.8%
2.38 5
 
2.8%
2.48 5
 
2.8%
2.1 4
 
2.2%
2.5 4
 
2.2%
Other values (69) 123
69.1%
ValueCountFrequency (%)
1.36 1
 
0.6%
1.7 2
1.1%
1.71 1
 
0.6%
1.75 1
 
0.6%
1.82 1
 
0.6%
1.88 1
 
0.6%
1.9 1
 
0.6%
1.92 3
1.7%
1.94 1
 
0.6%
1.95 1
 
0.6%
ValueCountFrequency (%)
3.23 1
0.6%
3.22 1
0.6%
2.92 1
0.6%
2.87 1
0.6%
2.86 1
0.6%
2.84 1
0.6%
2.8 1
0.6%
2.78 1
0.6%
2.75 1
0.6%
2.74 2
1.1%

Alcalinity_of_ash
Real number (ℝ)

High correlation 

Distinct63
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.494944
Minimum10.6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:35.296118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile14.77
Q117.2
median19.5
Q321.5
95-th percentile25
Maximum30
Range19.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.3395638
Coefficient of variation (CV)0.1713041
Kurtosis0.48794154
Mean19.494944
Median Absolute Deviation (MAD)2.05
Skewness0.21304689
Sum3470.1
Variance11.152686
MonotonicityNot monotonic
2025-05-07T12:14:35.372434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 15
 
8.4%
16 11
 
6.2%
21 11
 
6.2%
18 10
 
5.6%
19 9
 
5.1%
21.5 8
 
4.5%
18.5 7
 
3.9%
19.5 7
 
3.9%
22 7
 
3.9%
22.5 7
 
3.9%
Other values (53) 86
48.3%
ValueCountFrequency (%)
10.6 1
0.6%
11.2 1
0.6%
11.4 1
0.6%
12 1
0.6%
12.4 1
0.6%
13.2 1
0.6%
14 2
1.1%
14.6 1
0.6%
14.8 1
0.6%
15 2
1.1%
ValueCountFrequency (%)
30 1
 
0.6%
28.5 2
 
1.1%
27 1
 
0.6%
26.5 1
 
0.6%
26 1
 
0.6%
25.5 1
 
0.6%
25 5
2.8%
24.5 3
1.7%
24 5
2.8%
23.6 1
 
0.6%

Magnesium
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.741573
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:35.445200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80.85
Q188
median98
Q3107
95-th percentile124.3
Maximum162
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.282484
Coefficient of variation (CV)0.14319489
Kurtosis2.1049913
Mean99.741573
Median Absolute Deviation (MAD)10
Skewness1.0981911
Sum17754
Variance203.98934
MonotonicityNot monotonic
2025-05-07T12:14:35.514250image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 13
 
7.3%
86 11
 
6.2%
98 9
 
5.1%
101 9
 
5.1%
96 8
 
4.5%
102 7
 
3.9%
94 6
 
3.4%
85 6
 
3.4%
112 6
 
3.4%
97 5
 
2.8%
Other values (43) 98
55.1%
ValueCountFrequency (%)
70 1
 
0.6%
78 3
 
1.7%
80 5
 
2.8%
81 1
 
0.6%
82 1
 
0.6%
84 3
 
1.7%
85 6
3.4%
86 11
6.2%
87 3
 
1.7%
88 13
7.3%
ValueCountFrequency (%)
162 1
0.6%
151 1
0.6%
139 1
0.6%
136 1
0.6%
134 1
0.6%
132 1
0.6%
128 1
0.6%
127 1
0.6%
126 1
0.6%
124 1
0.6%

Total_phenols
Real number (ℝ)

High correlation 

Distinct97
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2951124
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:35.584517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.7425
median2.355
Q32.8
95-th percentile3.2745
Maximum3.88
Range2.9
Interquartile range (IQR)1.0575

Descriptive statistics

Standard deviation0.62585105
Coefficient of variation (CV)0.27268863
Kurtosis-0.83562652
Mean2.2951124
Median Absolute Deviation (MAD)0.505
Skewness0.086638586
Sum408.53
Variance0.39168954
MonotonicityNot monotonic
2025-05-07T12:14:35.654380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 8
 
4.5%
2.8 6
 
3.4%
3 6
 
3.4%
2.6 6
 
3.4%
2 5
 
2.8%
2.95 5
 
2.8%
1.65 4
 
2.2%
2.45 4
 
2.2%
2.85 4
 
2.2%
1.38 4
 
2.2%
Other values (87) 126
70.8%
ValueCountFrequency (%)
0.98 1
 
0.6%
1.1 1
 
0.6%
1.15 1
 
0.6%
1.25 1
 
0.6%
1.28 1
 
0.6%
1.3 1
 
0.6%
1.35 1
 
0.6%
1.38 4
2.2%
1.39 2
1.1%
1.4 2
1.1%
ValueCountFrequency (%)
3.88 1
 
0.6%
3.85 1
 
0.6%
3.52 1
 
0.6%
3.5 1
 
0.6%
3.4 1
 
0.6%
3.38 1
 
0.6%
3.3 3
1.7%
3.27 1
 
0.6%
3.25 2
1.1%
3.2 1
 
0.6%

Flavanoids
Real number (ℝ)

High correlation 

Distinct132
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0292697
Minimum0.34
Maximum5.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:35.722531image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.5455
Q11.205
median2.135
Q32.875
95-th percentile3.4975
Maximum5.08
Range4.74
Interquartile range (IQR)1.67

Descriptive statistics

Standard deviation0.99885869
Coefficient of variation (CV)0.4922257
Kurtosis-0.88038155
Mean2.0292697
Median Absolute Deviation (MAD)0.835
Skewness0.025343553
Sum361.21
Variance0.99771867
MonotonicityNot monotonic
2025-05-07T12:14:35.789883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65 4
 
2.2%
2.03 3
 
1.7%
2.68 3
 
1.7%
0.6 3
 
1.7%
1.25 3
 
1.7%
0.58 3
 
1.7%
2.53 2
 
1.1%
0.47 2
 
1.1%
0.66 2
 
1.1%
2.92 2
 
1.1%
Other values (122) 151
84.8%
ValueCountFrequency (%)
0.34 1
0.6%
0.47 2
1.1%
0.48 1
0.6%
0.49 1
0.6%
0.5 2
1.1%
0.51 1
0.6%
0.52 1
0.6%
0.55 1
0.6%
0.56 1
0.6%
0.57 1
0.6%
ValueCountFrequency (%)
5.08 1
0.6%
3.93 1
0.6%
3.75 1
0.6%
3.74 1
0.6%
3.69 1
0.6%
3.67 1
0.6%
3.64 1
0.6%
3.56 1
0.6%
3.54 1
0.6%
3.49 1
0.6%

Nonflavanoid_phenols
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36185393
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:35.852731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.19
Q10.27
median0.34
Q30.4375
95-th percentile0.6
Maximum0.66
Range0.53
Interquartile range (IQR)0.1675

Descriptive statistics

Standard deviation0.12445334
Coefficient of variation (CV)0.34393253
Kurtosis-0.63719106
Mean0.36185393
Median Absolute Deviation (MAD)0.085
Skewness0.45015134
Sum64.41
Variance0.015488634
MonotonicityNot monotonic
2025-05-07T12:14:35.916493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.43 11
 
6.2%
0.26 11
 
6.2%
0.29 10
 
5.6%
0.32 9
 
5.1%
0.27 8
 
4.5%
0.3 8
 
4.5%
0.34 8
 
4.5%
0.4 8
 
4.5%
0.37 8
 
4.5%
0.24 7
 
3.9%
Other values (29) 90
50.6%
ValueCountFrequency (%)
0.13 1
 
0.6%
0.14 2
 
1.1%
0.17 5
2.8%
0.19 2
 
1.1%
0.2 2
 
1.1%
0.21 6
3.4%
0.22 6
3.4%
0.24 7
3.9%
0.25 2
 
1.1%
0.26 11
6.2%
ValueCountFrequency (%)
0.66 1
 
0.6%
0.63 4
2.2%
0.61 3
1.7%
0.6 3
1.7%
0.58 3
1.7%
0.56 1
 
0.6%
0.55 1
 
0.6%
0.53 7
3.9%
0.52 5
2.8%
0.5 5
2.8%

Proanthocyanins
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5908989
Minimum0.41
Maximum3.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:36.053195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.73
Q11.25
median1.555
Q31.95
95-th percentile2.709
Maximum3.58
Range3.17
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.57235886
Coefficient of variation (CV)0.35977074
Kurtosis0.55464852
Mean1.5908989
Median Absolute Deviation (MAD)0.38
Skewness0.51713717
Sum283.18
Variance0.32759467
MonotonicityNot monotonic
2025-05-07T12:14:36.119674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.35 9
 
5.1%
1.46 7
 
3.9%
1.87 6
 
3.4%
1.25 5
 
2.8%
1.66 4
 
2.2%
2.08 4
 
2.2%
1.56 4
 
2.2%
1.98 4
 
2.2%
2.29 3
 
1.7%
1.14 3
 
1.7%
Other values (91) 129
72.5%
ValueCountFrequency (%)
0.41 1
0.6%
0.42 2
1.1%
0.55 1
0.6%
0.62 1
0.6%
0.64 2
1.1%
0.68 1
0.6%
0.73 2
1.1%
0.75 1
0.6%
0.8 2
1.1%
0.81 1
0.6%
ValueCountFrequency (%)
3.58 1
 
0.6%
3.28 1
 
0.6%
2.96 1
 
0.6%
2.91 2
1.1%
2.81 3
1.7%
2.76 1
 
0.6%
2.7 1
 
0.6%
2.5 1
 
0.6%
2.49 1
 
0.6%
2.45 1
 
0.6%

Color_intensity
Real number (ℝ)

High correlation 

Distinct132
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0580899
Minimum1.28
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:36.187154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.114
Q13.22
median4.69
Q36.2
95-th percentile9.598
Maximum13
Range11.72
Interquartile range (IQR)2.98

Descriptive statistics

Standard deviation2.3182859
Coefficient of variation (CV)0.45833228
Kurtosis0.38152227
Mean5.0580899
Median Absolute Deviation (MAD)1.51
Skewness0.86858479
Sum900.34
Variance5.3744494
MonotonicityNot monotonic
2025-05-07T12:14:36.254758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.6 4
 
2.2%
4.6 4
 
2.2%
3.8 4
 
2.2%
3.4 3
 
1.7%
5 3
 
1.7%
4.5 3
 
1.7%
5.4 3
 
1.7%
5.6 3
 
1.7%
3.05 3
 
1.7%
5.7 3
 
1.7%
Other values (122) 145
81.5%
ValueCountFrequency (%)
1.28 1
0.6%
1.74 1
0.6%
1.9 1
0.6%
1.95 2
1.1%
2 1
0.6%
2.06 2
1.1%
2.08 1
0.6%
2.12 1
0.6%
2.15 1
0.6%
2.2 1
0.6%
ValueCountFrequency (%)
13 1
0.6%
11.75 1
0.6%
10.8 1
0.6%
10.68 1
0.6%
10.52 1
0.6%
10.26 1
0.6%
10.2 1
0.6%
9.899999 1
0.6%
9.7 1
0.6%
9.58 1
0.6%

Hue
Real number (ℝ)

High correlation 

Distinct78
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95744944
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:36.321717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.7825
median0.965
Q31.12
95-th percentile1.2845
Maximum1.71
Range1.23
Interquartile range (IQR)0.3375

Descriptive statistics

Standard deviation0.22857157
Coefficient of variation (CV)0.23872965
Kurtosis-0.34409574
Mean0.95744944
Median Absolute Deviation (MAD)0.165
Skewness0.021091272
Sum170.426
Variance0.052244961
MonotonicityNot monotonic
2025-05-07T12:14:36.391105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.04 8
 
4.5%
1.23 7
 
3.9%
1.12 6
 
3.4%
0.57 5
 
2.8%
0.89 5
 
2.8%
0.96 5
 
2.8%
1.25 5
 
2.8%
0.75 4
 
2.2%
1.05 4
 
2.2%
1.19 4
 
2.2%
Other values (68) 125
70.2%
ValueCountFrequency (%)
0.48 1
 
0.6%
0.54 1
 
0.6%
0.55 1
 
0.6%
0.56 2
 
1.1%
0.57 5
2.8%
0.58 2
 
1.1%
0.59 2
 
1.1%
0.6 3
1.7%
0.61 2
 
1.1%
0.62 1
 
0.6%
ValueCountFrequency (%)
1.71 1
 
0.6%
1.45 1
 
0.6%
1.42 1
 
0.6%
1.38 1
 
0.6%
1.36 2
 
1.1%
1.33 1
 
0.6%
1.31 2
 
1.1%
1.28 2
 
1.1%
1.27 1
 
0.6%
1.25 5
2.8%

0D280_0D315_of_diluted_wines
Real number (ℝ)

High correlation 

Distinct122
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6116854
Minimum1.27
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:36.454491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.4625
Q11.9375
median2.78
Q33.17
95-th percentile3.58
Maximum4
Range2.73
Interquartile range (IQR)1.2325

Descriptive statistics

Standard deviation0.70999043
Coefficient of variation (CV)0.27185144
Kurtosis-1.0864345
Mean2.6116854
Median Absolute Deviation (MAD)0.52
Skewness-0.3072855
Sum464.88
Variance0.50408641
MonotonicityNot monotonic
2025-05-07T12:14:36.519764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.87 5
 
2.8%
1.82 4
 
2.2%
3 4
 
2.2%
2.78 4
 
2.2%
1.56 3
 
1.7%
1.75 3
 
1.7%
2.77 3
 
1.7%
2.31 3
 
1.7%
1.33 3
 
1.7%
3.33 3
 
1.7%
Other values (112) 143
80.3%
ValueCountFrequency (%)
1.27 1
 
0.6%
1.29 2
1.1%
1.3 1
 
0.6%
1.33 3
1.7%
1.36 1
 
0.6%
1.42 1
 
0.6%
1.47 1
 
0.6%
1.48 1
 
0.6%
1.51 2
1.1%
1.55 1
 
0.6%
ValueCountFrequency (%)
4 1
0.6%
3.92 1
0.6%
3.82 1
0.6%
3.71 1
0.6%
3.69 1
0.6%
3.64 1
0.6%
3.63 1
0.6%
3.59 1
0.6%
3.58 2
1.1%
3.57 1
0.6%

Proline
Real number (ℝ)

High correlation 

Distinct121
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.89326
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-07T12:14:36.585360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile354.55
Q1500.5
median673.5
Q3985
95-th percentile1297.25
Maximum1680
Range1402
Interquartile range (IQR)484.5

Descriptive statistics

Standard deviation314.90747
Coefficient of variation (CV)0.42162313
Kurtosis-0.24840311
Mean746.89326
Median Absolute Deviation (MAD)202.5
Skewness0.76782178
Sum132947
Variance99166.717
MonotonicityNot monotonic
2025-05-07T12:14:36.653619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
680 5
 
2.8%
520 5
 
2.8%
625 4
 
2.2%
750 4
 
2.2%
630 4
 
2.2%
1035 3
 
1.7%
562 3
 
1.7%
495 3
 
1.7%
660 3
 
1.7%
510 3
 
1.7%
Other values (111) 141
79.2%
ValueCountFrequency (%)
278 1
0.6%
290 1
0.6%
312 1
0.6%
315 1
0.6%
325 1
0.6%
342 1
0.6%
345 2
1.1%
352 1
0.6%
355 1
0.6%
365 1
0.6%
ValueCountFrequency (%)
1680 1
0.6%
1547 1
0.6%
1515 1
0.6%
1510 1
0.6%
1480 1
0.6%
1450 1
0.6%
1375 1
0.6%
1320 1
0.6%
1310 1
0.6%
1295 1
0.6%

class
Categorical

High correlation 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
2
71 
1
59 
3
48 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters178
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 71
39.9%
1 59
33.1%
3 48
27.0%

Length

2025-05-07T12:14:36.715503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-07T12:14:36.764291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2 71
39.9%
1 59
33.1%
3 48
27.0%

Most occurring characters

ValueCountFrequency (%)
2 71
39.9%
1 59
33.1%
3 48
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 71
39.9%
1 59
33.1%
3 48
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 71
39.9%
1 59
33.1%
3 48
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 71
39.9%
1 59
33.1%
3 48
27.0%

Interactions

2025-05-07T12:14:33.992679image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:26.793350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.502481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.133383image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.725039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.353076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.974858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.539697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.067678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.613795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.263158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.879090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.434846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:34.039332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:26.885248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.635039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.181077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.775167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.400182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.022658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.583486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.112221image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.662517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.316792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.926225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.483710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:34.081649image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:26.951016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.675277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.225075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.819637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.438830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.064276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.623646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.152783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.703328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.398393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.967949image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.524257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:34.126247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.030292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.720070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.270854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.867973image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.482899image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.110505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.666012image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.195758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.749431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.447003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.013595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.572285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:34.174139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.094699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.765746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.322525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.918125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.528763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.157028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.712178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.243169image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.796849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.495438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.064182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.619021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:34.213755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.139102image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.805290image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.364874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.960067image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.567977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.198543image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.749175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.281287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.838582image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.535880image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.104081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.659789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:34.258151image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.186129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.846878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.413212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.040553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.609924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.242011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.791024image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.323653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.881475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.581935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.148181image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.702640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:34.297082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.229957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.885349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.455569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.083972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.646842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.281911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.827843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.361737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.921849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.621434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.186650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.742763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:34.337216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.273330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.924161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.500600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.128137image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.686767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.321707image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.866496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.400893image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.961823image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.662407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.227506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.782306image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:34.379983image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.320948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.968192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.547474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.173233image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.728241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.366539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.907147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.443239image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.004751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.706764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.269993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.826120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:34.421001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.367516image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.007953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.592248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.218639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.771836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.408070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.946679image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.485454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.047132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.749301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.311405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.866564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:34.548811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.411870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.049202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.635714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.262599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.811986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.451168image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.985859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.526300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.088253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.791004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.350342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.909097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:34.591173image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:27.457198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.090112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:28.679580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.307974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:29.932708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:30.493246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.027068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:31.572017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.209492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:32.834193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.392606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-05-07T12:14:33.949188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2025-05-07T12:14:36.808120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AlcoholMalicacidAshAlcalinity_of_ashMagnesiumTotal_phenolsFlavanoidsNonflavanoid_phenolsProanthocyaninsColor_intensityHue0D280_0D315_of_diluted_winesProlineclass
Alcohol1.0000.0940.212-0.3100.2710.2890.237-0.1560.1370.546-0.0720.0720.644-0.328
Malicacid0.0941.0000.1640.289-0.055-0.335-0.4110.293-0.2210.249-0.561-0.369-0.1920.438
Ash0.2120.1641.0000.4430.2870.1290.1150.1860.0100.259-0.0750.0040.224-0.050
Alcalinity_of_ash-0.3100.2890.4431.000-0.083-0.321-0.3510.362-0.1970.019-0.274-0.277-0.4410.518
Magnesium0.271-0.0550.287-0.0831.0000.2140.196-0.2560.2360.2000.0550.0660.393-0.209
Total_phenols0.289-0.3350.129-0.3210.2141.0000.865-0.4500.612-0.0550.4340.7000.498-0.719
Flavanoids0.237-0.4110.115-0.3510.1960.8651.000-0.5380.653-0.1720.5430.7870.494-0.847
Nonflavanoid_phenols-0.1560.2930.1860.362-0.256-0.450-0.5381.000-0.3660.139-0.263-0.503-0.3110.489
Proanthocyanins0.137-0.2210.010-0.1970.2360.6120.653-0.3661.000-0.0250.2960.5190.330-0.499
Color_intensity0.5460.2490.2590.0190.200-0.055-0.1720.139-0.0251.000-0.522-0.4290.3160.266
Hue-0.072-0.561-0.075-0.2740.0550.4340.543-0.2630.296-0.5221.0000.5650.236-0.617
0D280_0D315_of_diluted_wines0.072-0.3690.004-0.2770.0660.7000.787-0.5030.519-0.4290.5651.0000.313-0.788
Proline0.644-0.1920.224-0.4410.3930.4980.494-0.3110.3300.3160.2360.3131.000-0.634
class-0.3280.438-0.0500.518-0.209-0.719-0.8470.489-0.4990.266-0.617-0.788-0.6341.000
2025-05-07T12:14:36.892890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AlcoholMalicacidAshAlcalinity_of_ashMagnesiumTotal_phenolsFlavanoidsNonflavanoid_phenolsProanthocyaninsColor_intensityHue0D280_0D315_of_diluted_winesProlineclass
Alcohol1.0000.1400.244-0.3070.3660.3110.295-0.1620.1930.635-0.0240.1030.634-0.354
Malicacid0.1401.0000.2310.3040.080-0.280-0.3250.255-0.2450.290-0.560-0.255-0.0570.347
Ash0.2440.2311.0000.3660.3610.1320.0790.1460.0240.283-0.050-0.0070.253-0.054
Alcalinity_of_ash-0.3070.3040.3661.000-0.170-0.377-0.4440.389-0.254-0.074-0.353-0.326-0.4560.570
Magnesium0.3660.0800.361-0.1701.0000.2460.233-0.2370.1740.3570.0360.0570.508-0.250
Total_phenols0.311-0.2800.132-0.3770.2461.0000.879-0.4480.6670.0110.4390.6870.419-0.727
Flavanoids0.295-0.3250.079-0.4440.2330.8791.000-0.5440.730-0.0430.5350.7420.430-0.855
Nonflavanoid_phenols-0.1620.2550.1460.389-0.237-0.448-0.5441.000-0.3850.060-0.268-0.495-0.2700.474
Proanthocyanins0.193-0.2450.024-0.2540.1740.6670.730-0.3851.000-0.0310.3430.5540.308-0.571
Color_intensity0.6350.2900.283-0.0740.3570.011-0.0430.060-0.0311.000-0.419-0.3180.4570.131
Hue-0.024-0.560-0.050-0.3530.0360.4390.535-0.2680.343-0.4191.0000.4850.208-0.617
0D280_0D315_of_diluted_wines0.103-0.255-0.007-0.3260.0570.6870.742-0.4950.554-0.3180.4851.0000.253-0.744
Proline0.634-0.0570.253-0.4560.5080.4190.430-0.2700.3080.4570.2080.2531.000-0.576
class-0.3540.347-0.0540.570-0.250-0.727-0.8550.474-0.5710.131-0.617-0.744-0.5761.000
2025-05-07T12:14:36.979176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AlcoholMalicacidAshAlcalinity_of_ashMagnesiumTotal_phenolsFlavanoidsNonflavanoid_phenolsProanthocyaninsColor_intensityHue0D280_0D315_of_diluted_winesProlineclass
Alcohol1.0000.0940.170-0.2130.2510.2090.191-0.1100.1340.434-0.0220.0620.449-0.239
Malicacid0.0941.0000.1580.2100.051-0.175-0.2120.175-0.1690.196-0.389-0.163-0.0450.247
Ash0.1700.1581.0000.2580.2540.0900.0490.0990.0180.188-0.037-0.0060.172-0.038
Alcalinity_of_ash-0.2130.2100.2581.000-0.121-0.257-0.3100.278-0.171-0.057-0.239-0.226-0.3130.449
Magnesium0.2510.0510.254-0.1211.0000.1720.162-0.1580.1180.2420.0240.0340.343-0.185
Total_phenols0.209-0.1750.090-0.2570.1721.0000.702-0.3100.4670.0280.2890.4780.280-0.590
Flavanoids0.191-0.2120.049-0.3100.1620.7021.000-0.3780.5350.0290.3540.5200.264-0.725
Nonflavanoid_phenols-0.1100.1750.0990.278-0.158-0.310-0.3781.000-0.2690.036-0.180-0.364-0.1740.379
Proanthocyanins0.134-0.1690.018-0.1710.1180.4670.535-0.2691.000-0.0150.2310.3690.204-0.450
Color_intensity0.4340.1960.188-0.0570.2420.0280.0290.036-0.0151.000-0.292-0.2060.3170.065
Hue-0.022-0.389-0.037-0.2390.0240.2890.354-0.1800.231-0.2921.0000.3250.144-0.479
0D280_0D315_of_diluted_wines0.062-0.163-0.006-0.2260.0340.4780.520-0.3640.369-0.2060.3251.0000.152-0.608
Proline0.449-0.0450.172-0.3130.3430.2800.264-0.1740.2040.3170.1440.1521.000-0.406
class-0.2390.247-0.0380.449-0.185-0.590-0.7250.379-0.4500.065-0.479-0.608-0.4061.000
2025-05-07T12:14:37.066496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AlcoholMalicacidAshAlcalinity_of_ashMagnesiumTotal_phenolsFlavanoidsNonflavanoid_phenolsProanthocyaninsColor_intensityHue0D280_0D315_of_diluted_winesProlineclass
Alcohol1.0000.2640.2790.4690.3270.4880.4750.1460.5240.6440.6250.2500.6360.727
Malicacid0.2641.0000.3020.3600.0000.5130.3940.3570.4420.3260.4290.4880.3600.657
Ash0.2790.3021.0000.5530.4510.0960.4770.3320.3120.0000.0000.1840.0000.354
Alcalinity_of_ash0.4690.3600.5531.0000.3180.4980.4920.2610.5160.4400.2840.1070.5410.546
Magnesium0.3270.0000.4510.3181.0000.2720.3270.2700.6300.4590.0000.2820.5230.569
Total_phenols0.4880.5130.0960.4980.2721.0000.7230.4940.6390.4670.3110.6610.6820.708
Flavanoids0.4750.3940.4770.4920.3270.7231.0000.4520.5250.5620.6150.6010.6030.961
Nonflavanoid_phenols0.1460.3570.3320.2610.2700.4940.4521.0000.1860.3060.3750.5850.4050.518
Proanthocyanins0.5240.4420.3120.5160.6300.6390.5250.1861.0000.4230.2400.4970.6760.564
Color_intensity0.6440.3260.0000.4400.4590.4670.5620.3060.4231.0000.4690.5060.5250.780
Hue0.6250.4290.0000.2840.0000.3110.6150.3750.2400.4691.0000.5500.3190.871
0D280_0D315_of_diluted_wines0.2500.4880.1840.1070.2820.6610.6010.5850.4970.5060.5501.0000.4790.777
Proline0.6360.3600.0000.5410.5230.6820.6030.4050.6760.5250.3190.4791.0000.777
class0.7270.6570.3540.5460.5690.7080.9610.5180.5640.7800.8710.7770.7771.000
2025-05-07T12:14:37.148094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
0D280_0D315_of_diluted_winesAlcalinity_of_ashAlcoholAshColor_intensityFlavanoidsHueMagnesiumMalicacidNonflavanoid_phenolsProanthocyaninsProlineTotal_phenolsclass
0D280_0D315_of_diluted_wines1.000-0.3260.103-0.007-0.3180.7420.4850.057-0.255-0.4950.5540.2530.6870.645
Alcalinity_of_ash-0.3261.000-0.3070.366-0.074-0.444-0.353-0.1700.3040.389-0.254-0.456-0.3770.382
Alcohol0.103-0.3071.0000.2440.6350.295-0.0240.3660.140-0.1620.1930.6340.3110.581
Ash-0.0070.3660.2441.0000.2830.079-0.0500.3610.2310.1460.0240.2530.1320.222
Color_intensity-0.318-0.0740.6350.2831.000-0.043-0.4190.3570.2900.060-0.0310.4570.0110.649
Flavanoids0.742-0.4440.2950.079-0.0431.0000.5350.233-0.325-0.5440.7300.4300.8790.752
Hue0.485-0.353-0.024-0.050-0.4190.5351.0000.036-0.560-0.2680.3430.2080.4390.583
Magnesium0.057-0.1700.3660.3610.3570.2330.0361.0000.080-0.2370.1740.5080.2460.403
Malicacid-0.2550.3040.1400.2310.290-0.325-0.5600.0801.0000.255-0.245-0.057-0.2800.496
Nonflavanoid_phenols-0.4950.389-0.1620.1460.060-0.544-0.268-0.2370.2551.000-0.385-0.270-0.4480.355
Proanthocyanins0.554-0.2540.1930.024-0.0310.7300.3430.174-0.245-0.3851.0000.3080.6670.399
Proline0.253-0.4560.6340.2530.4570.4300.2080.508-0.057-0.2700.3081.0000.4190.644
Total_phenols0.687-0.3770.3110.1320.0110.8790.4390.246-0.280-0.4480.6670.4191.0000.560
class0.6450.3820.5810.2220.6490.7520.5830.4030.4960.3550.3990.6440.5601.000

Missing values

2025-05-07T12:14:34.665244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-07T12:14:34.767605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AlcoholMalicacidAshAlcalinity_of_ashMagnesiumTotal_phenolsFlavanoidsNonflavanoid_phenolsProanthocyaninsColor_intensityHue0D280_0D315_of_diluted_winesProlineclass
014.231.712.4315.61272.803.060.282.295.641.043.9210651
113.201.782.1411.21002.652.760.261.284.381.053.4010501
213.162.362.6718.61012.803.240.302.815.681.033.1711851
314.371.952.5016.81133.853.490.242.187.800.863.4514801
413.242.592.8721.01182.802.690.391.824.321.042.937351
514.201.762.4515.21123.273.390.341.976.751.052.8514501
614.391.872.4514.6962.502.520.301.985.251.023.5812901
714.062.152.6117.61212.602.510.311.255.051.063.5812951
814.831.642.1714.0972.802.980.291.985.201.082.8510451
913.861.352.2716.0982.983.150.221.857.221.013.5510451
AlcoholMalicacidAshAlcalinity_of_ashMagnesiumTotal_phenolsFlavanoidsNonflavanoid_phenolsProanthocyaninsColor_intensityHue0D280_0D315_of_diluted_winesProlineclass
16813.582.582.6924.51051.550.840.391.548.6600000.741.807503
16913.404.602.8625.01121.980.960.271.118.5000000.671.926303
17012.203.032.3219.0961.250.490.400.735.5000000.661.835103
17112.772.392.2819.5861.390.510.480.649.8999990.571.634703
17214.162.512.4820.0911.680.700.441.249.7000000.621.716603
17313.715.652.4520.5951.680.610.521.067.7000000.641.747403
17413.403.912.4823.01021.800.750.431.417.3000000.701.567503
17513.274.282.2620.01201.590.690.431.3510.2000000.591.568353
17613.172.592.3720.01201.650.680.531.469.3000000.601.628403
17714.134.102.7424.5962.050.760.561.359.2000000.611.605603